Predicting suitable habitats of Melia azedarach L. in China using data mining

Melia azedarach L. is an important economic tree widely distributed in tropical and subtropical regions of China and some other countries. However, it is unclear how the species’ suitable habitat will respond to future climate changes. We aimed to select the most accurate one among seven data mining...

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Bibliographic Details
Published in:Scientific reports 2022-07, Vol.12 (1), p.12617-12617, Article 12617
Main Authors: Feng, Lei, Tian, Xiangni, El-Kassaby, Yousry A., Qiu, Jian, Feng, Ze, Sun, Jiejie, Wang, Guibin, Wang, Tongli
Format: Article
Language:English
Subjects:
631/158/1144
631/158/1145
631/158/2165
631/158/2456
704/106/694
704/158/1144
704/158/1145
704/158/2165
704/158/2456
Bayesian analysis
Climate change
Climate prediction
Data mining
Entropy
Environmental impact
Generalized linear models
Habitats
Humanities and Social Sciences
Mathematical models
Maximum entropy
Melia azedarach
multidisciplinary
Science
Science (multidisciplinary)
Species
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Summary:Melia azedarach L. is an important economic tree widely distributed in tropical and subtropical regions of China and some other countries. However, it is unclear how the species’ suitable habitat will respond to future climate changes. We aimed to select the most accurate one among seven data mining models to predict the current and future suitable habitats for M. azedarach in China. These models include: maximum entropy (MaxEnt), support vector machine (SVM), generalized linear model (GLM), random forest (RF), naive bayesian model (NBM), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM). A total of 906  M . azedarach locations were identified, and sixteen climate predictors were used for model building. The models’ validity was assessed using three measures (Area Under the Curves (AUC), kappa, and overall accuracy (OA)). We found that the RF provided the most outstanding performance in prediction power and generalization capacity. The top climate factors affecting the species’ suitable habitats were mean coldest month temperature (MCMT), followed by the number of frost-free days (NFFD), degree-days above 18 °C (DD > 18), temperature difference between MWMT and MCMT, or continentality (TD), mean annual precipitation (MAP), and degree-days below 18 °C (DD 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-16571-y